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| Template Creation Time (s): 1.0 ± 0.8 |
| Search Time (s): 0.14 ± 0.03 |
| FNIR (@ FPIR=0.01): 0.0030 ± 0.0004 (± 90% confidence) |
| FNIR (@ FPIR=0.001): 0.0034 ± 0.0004 (± 90% confidence) |
| Miss Rate @ Rank 1: 0.0029 |
| Miss Rate @ Rank 10: 0.0025 |
| Miss Rate @ Rank 100: 0.0021 |
| Failure to Enroll (FTE) Rate: 0.00002 |
| Dataset: | Operational Dataset 4th pull (stats on OPS4 images) |
| Samples used: | Both eyes |
| Enrolled Population: | 500K people |
| Enrollment Method: | Both (left and right) iris images per enrollment template |
| FNIR (@ FPIR=0.01): 0.0096 ± 0.0005 (± 90% confidence) |
| FNIR (@ FPIR=0.001): 0.0117 ± 0.0006 (± 90% confidence) |
| Miss Rate @ Rank 1: 0.0090 |
| Miss Rate @ Rank 10: 0.0077 |
| Miss Rate @ Rank 100: 0.0066 |
| Failure to Enroll (FTE) Rate: 0.0003 |
| Dataset: | Operational Dataset 4th pull (stats on OPS4 images) |
| Samples used: | One eye |
| Enrolled Population: | 1M irides (500K people) |
| Enrollment Method: | One iris image per enrollment template |
Core accuracy for the identification task can be characterized by Detection-error trade-off (DET) plots. Generally, curves lower down in a DET plot correspond to more accurate matchers. The plots are interactive through the use of the Plotly.js graphing library.
Two-eye Accuracy
| Dataset: | Operational Dataset 4th pull (stats on OPS4 images) |
| Samples used: | Both eyes |
| Enrolled Population: | 500K people |
| Enrollment Method: | Both (left and right) iris images per enrollment template |
Single Eye Accuracy
| Dataset: | Operational Dataset 4th pull (stats on OPS4 images) |
| Samples used: | One eye |
| Enrolled Population: | 1M irides (500K people) |
| Enrollment Method: | One iris image per enrollment template |
Two-eye Accuracy
| Dataset: | Operational Dataset 4th pull (stats on OPS4 images) |
| Samples used: | Both eyes |
| Enrolled Population: | 500K people |
| Enrollment Method: | Both (left and right) iris images per enrollment template |
Single-eye Accuracy
| Dataset: | Operational Dataset 4th pull (stats on OPS4 images) |
| Samples used: | One eye |
| Enrolled Population: | 1M irides (500K people) |
| Enrollment Method: | One iris image per enrollment template |
Accuracy is impacted by the size of the enrollment database (a.k.a the gallery size). Identification of the correct mate is expected to be more difficult for larger enrollment database sizes. The figure below plots FNIR (at FPIR=\(0.01\)) as a function of enrollment database size.
| Dataset: | Operational Dataset 4th pull (stats on OPS4 images) |
| Accuracy Metric: | FNIR (i.e., “miss rate”) at an FPIR of 0.01 |
| Samples used: | Both eyes |
| Enrollment Method: | One enrollment session per person |
Some apparant trends may be the result of random variation. Results for the 10K and 50K enrollment sizes were computed from 140K searches. Results for the 100K and 500K enrollment sizes were computed from 700K searches.
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